Kepler Presearch Data Conditioning II - A Bayesian Approach to Systematic Error Correction

ABSTRACT With the unprecedented photometric precision of the Kepler spacecraft, significant systematic and stochastic errors on transit signal levels are observable in the Kepler photometric data. These errors, which include discontinuities, outliers, systematic trends, and other instrumental signat...

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Bibliographic Details
Published in:Publications of the Astronomical Society of the Pacific 2012-09, Vol.124 (919), p.1000-1014
Main Authors: Smith, Jeffrey C., Stumpe, Martin C., Van Cleve, Jeffrey E., Jenkins, Jon M., Barclay, Thomas S., Fanelli, Michael N., Girouard, Forrest R., Kolodziejczak, Jeffery J., McCauliff, Sean D., Morris, Robert L., Twicken, Joseph D.
Format: Article
Language:English
Subjects:
Acoustic waves
Astronomy
Data analysis
Earth, ocean, space
Exact sciences and technology
Noise levels
Noise pollution
Noise reduction
Photometric observations
Pipelines
Spacecraft
Stellar investigations
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Summary:ABSTRACT With the unprecedented photometric precision of the Kepler spacecraft, significant systematic and stochastic errors on transit signal levels are observable in the Kepler photometric data. These errors, which include discontinuities, outliers, systematic trends, and other instrumental signatures, obscure astrophysical signals. The presearch data conditioning (PDC) module of the Kepler data analysis pipeline tries to remove these errors while preserving planet transits and other astrophysically interesting signals. The completely new noise and stellar variability regime observed in Kepler data poses a significant problem to standard cotrending methods. Variable stars are often of particular astrophysical interest, so the preservation of their signals is of significant importance to the astrophysical community. We present a Bayesian maximum a posteriori (MAP) approach, where a subset of highly correlated and quiet stars is used to generate a cotrending basis vector set, which is in turn used to establish a range of "reasonable" robust fit parameters. These robust fit parameters are then used to generate a Bayesian prior and a Bayesian posterior probability distribution function (PDF) which, when maximized, finds the best fit that simultaneously removes systematic effects while reducing the signal distortion and noise injection that commonly afflicts simple least-squares (LS) fitting. A numerical and empirical approach is taken where the Bayesian prior PDFs are generated from fits to the light-curve distributions themselves.
ISSN:0004-6280
1538-3873
DOI:10.1086/667697